It is known that individuals can be identified by means of prints that are characteristic of that individual. These prints include unique patterns of friction ridges and other features on the surface of the fingers, palms, toes and feet. When discovered, and properly examined, these prints can lead to proof of a person's past location.
A subset of these prints are traces left on objects by the physical interaction of the palm/fingers/etc. Such traces consist of perspiration and oils, due to the excretion of perspiration through pores on the palm and fingers. A variety of forensic techniques exist to recover these traces from different types of objects. These recovered impressions are known as latent prints.
Prints also include impressed prints. Impressed prints are typically physical impressions left in a malleable material, for example an indentation left by a finger.
Another kind of print is a patent print. These prints are typically visible to the naked eye. One example is a finger print left on a surface resulting from the finger having previously been coated with ink or other substance leaving a visible mark.
For the purpose of this document, a print can also include the pattern of features on an actual hand or foot (hereinafter an “actual print”), for example as appearing in a photograph of a person's hand or foot.
Prints can contain a variety of different types of features, which are broadly classified into level I, level II or level III detail. Level I detail includes attributes of friction ridges of the finger/palm/etc., and includes deltas (also known as a triradius), loops, whorls and, in the case of palm prints, vestiges. Level II detail describe minutiae points relating to friction ridges, including end points, bifurcations, ridge units/short friction ridges, and friction ridge islands. Level III detail includes other features, including flexation creases, pores and scars.
Images can be recorded of latent/impressed/patent/actual prints, for example using photography or other image capture means. These images can then be analysed for the purpose of identifying the person to whom the print belongs.
A print which contains sufficient discriminating physical characteristics can be matched to an individual, hence proving that a person held or touched an object on which the print was found, or similarly proving that an actual finger/palm etc. shown in a photograph belongs to that person. To match a print, a print of the original palm or fingers is required. These original prints are known as exemplar prints and are stored in a database. The process of matching a print to an exemplar print (known as latent to full matching in the case of latent print matching) involves individually comparing the print with each of the prints stored in the database, until a match is obtained. However, in practice, owing to the many means of contact between the palm and object and the particular method of acquisition, the quality of an individual print varies dramatically. Therefore, the complexity of the identification of the features and the matching process is not a trivial task.
AFIS (Automatic Fingerprint Identification Systems) and APIS (Automatic Palmprint Identification Systems) are used to automatically find a set of exemplar prints which best match latent prints, resulting in a ranked list of exemplar prints. Known AFIS/APIS techniques typically require any matches to be verified by a human.
One step performed by AFIS/APIS is to determine the orientation of features in the image of a latent print. This orientation is estimated by performing one of a slit based method, a frequency domain method, or a gradient method. However, these methods have disadvantages.
Slit based methods use a pair of orthogonal slits, which are placed at different positions on an image of a latent print. The greyscale values of the print pattern along each slit are measured. The slits are then rotated to a number of different angles, and the greyscale values re-measured. From the variations in greyscale along each of the slits for each of the angles, the orientation of the friction ridge at that point can be determined. This is then repeated across the rest of the image. Slit based methods provide discrete results, which have non-trivial associated errors that are dependent on the angles chosen for orientating the slits during the analysis. Moreover, slit-based methods are computationally complex, thus placing high demands on data processing resources available at computing devices.
Frequency domain methods involve splitting an image of a latent print into discrete blocks, and performing a discrete Fourier transform on each block. The resulting frequency peaks can be used to infer a number of possible orientations within the each of the blocks. As with slit based methods, frequency based methods yield discrete results, and accordingly have an associated error. Moreover, frequency based methods are also computationally complex, and place high demands on data processing resources available at computing devices.
Gradient methods involve calculating the rate of change of features in an image of a latent print in x- and y-directions. Whilst this technique is known to produce good results when applied to good quality palm prints, it is less accurate when analysing areas of low rates of change in images of latent prints.
Standard AFIS/APIS techniques are also typically limited to the analysis of a small subset of the different types of features that make up a latent print, in particular being able to only perform analysis of a subset of level I features.
Accordingly, there is a desire for an improved automatic print analysis.